With the development of wireless sensor nodes and sensors in wearable textiles the demand for electronic components with low power consumption has been significantly increasing. For example, a development objective consists of achieving a power consumption in electronic components in the range below one micro-watt for many applications, for example for the always-on functionality in textiles and the intelligent signal processing for integrated sensors for use in Industry 4.0 scenarios.
The analysis of sensor signals requires the extraction of useful information or parameters, which can also be referred to as “features” of the signal. Although the sampling or digitization of the signal at the Nyquist rate or above ensures the (complete) reconstruction of the signal, important features can also be obtained from processing at a lower frequency or rate (i.e., sampling frequency or sampling rate) of the signal. For example, in most real-world applications the sampling rate for audio recorders at a frequency of 8000 Hz to 44,100 Hertz can be sufficient. In an intelligent implementation, however, the rate of the “digitization” can also take place at a sampling frequency which is reduced by an order of magnitude.
For example, a speech activity recognition system can be based on an analog filter bank, in which the signal is decomposed into different spectral components. The features used for the speech recognition are then, for example, the energies in each frequency band. The sampling frequency can be reduced to 640 hertz, which in an example recognition system investigated would lead to a maximum power consumption of 6 microwatt.
A feature vector obtained in this way, which obtains and/or represents the properties/energies in each frequency band or spectral range, is then passed to a classifier, which discriminates speech from other signals on the basis of a decision tree. A microcontroller is then used to select, on the basis of a current signal-to-noise ratio and noise type, which features are useful for further analysis. This means that the signals from all channels/frequency ranges must be passed through a multiplexer, digitized using an analog-to-digital converter and then transferred to the classifier. However, not all features/energies are usually used for the classification for the entire time.